Neighborhood aware load balancing

ABSTRACT

Systems, methods, computer-readable media, and devices are disclosed for collecting access point telemetry. A first access point is identified that is associated with a single instance on a pod. A hash identifier is identified, where the hash identifier identifies a radio frequency (RF) neighborhood of the first access point based on a geographical location of the first access point. Subsequent access point members of the RF neighborhood are dynamically determined by dynamically assigning a second access point to the RF neighborhood, the dynamic assignment based on the second access point being within a threshold geographical location to the first access point. Telemetry from the second access point is directed towards the single instance on the pod, where the pod receives telemetry for all access points in the dynamically determined RF neighborhood.

TECHNICAL FIELD

The present disclosure relates generally to the collection of telemetry,and more specifically, to directing telemetry towards an instance on aspecific network device.

BACKGROUND

With the proliferation of wireless network deployments, many vendors inthe WLAN space offer cloud based management services that enable themanagement of several hundreds or even thousands of Access Points (APs)in a “single pane of glass” dashboard. All of the Cloud-based WLANmanagement solutions have a dedicated resource management embedded tomanage complex problems pertaining to Wi-Fi deployments. Since therecould be multiple instances of the resource management runningside-by-side at any given time, it is desirable to have telemetrytraffic from all APs in a given radio frequency neighborhood (RFN)going/redirected to the same service instance in order to reduce overalllatency.

However existing load balancing techniques fail to achieve thischallenge. Management services perform load-balancing optimizationpurely based on the network load metrics and do not take intoconsideration inter-node proximity.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-recited and other advantages and features of the presenttechnology will become apparent by reference to specific implementationsillustrated in the appended drawings. A person of ordinary skill in theart will understand that these drawings only show some examples of thepresent technology and would not limit the scope of the presenttechnology to these examples. Furthermore, the skilled artisan willappreciate the principles of the present technology as described andexplained with additional specificity and detail through the use of theaccompanying drawings in which:

FIG. 1 illustrates an example network environment for collectingtelemetry from access points in accordance with some embodiments;

FIG. 2 is a flow chart illustrating an example embodiment of a telemetrymanagement service in accordance with some embodiments;

FIG. 3 illustrates example radio frequency (RF) neighborhoods inaccordance with some embodiments;

FIG. 4 illustrates an example network environment for collectingtelemetry from access points in accordance with some embodiments;

FIG. 5 illustrates an example network environment for collectingtelemetry from access points in accordance with some embodiments; and

FIG. 6 shows an example of a system for implementing certain aspects ofthe present technology in accordance with some embodiments.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Various examples of the present technology are discussed in detailbelow. While specific implementations are discussed, it should beunderstood that this is done for illustration purposes only. A personskilled in the relevant art will recognize that other components andconfigurations may be used without parting from the spirit and scope ofthe present technology.

Overview:

Systems, methods, computer-readable media, and devices are disclosed forcollecting access point telemetry. A first access point is identifiedthat is associated with a single instance on a pod. A hash identifier isidentified, where the hash identifier identifies a radio frequency (RF)neighborhood of the first access point based on a geographical locationof the first access point. Subsequent access point members of the RFneighborhood are dynamically determined by dynamically assigning asecond access point to the RF neighborhood, the dynamic assignment basedon the second access point being within a threshold geographicallocation to the first access point. Telemetry from the second accesspoint is directed towards the single instance on the pod, where the podreceives telemetry for all access points in the dynamically determinedRF neighborhood.

Example Embodiments

The disclosed technology addresses the need in the art for a loadbalancing technique that can optimize resource utilization, maximizethroughput, reduce latency, and ensure fault-tolerant configurations forlocalized radio frequency (RF) sectors and thereby ensure optimalload-balancing for Wi-Fi access nodes located in either a singlegeographical area or with similar characteristics into similarcontainers.

In some embodiments, a method for collecting access point telemetryincludes identifying a first access point associated with a singleinstance on a pod. The method further includes identifying a hashidentifier for a radio frequency (RF) neighborhood of the first accesspoint based on a geographical location of the first access point. Themethod additionally includes, dynamically determining subsequent accesspoint members of the RF neighborhood by dynamically assigning a secondaccess point to the RF neighborhood based on the second access pointbeing within a threshold geographical location to the first accesspoint. The method also includes directing telemetry from the secondaccess point towards the single instance on the pod, wherein the podreceives telemetry for all access points in the dynamically determinedRF neighborhood.

In some embodiments, the threshold geographical location is based on anidentification by the second access point that the second access pointcan hear a signal from the first access point at a threshold strength.

In some embodiments, the method also includes receiving, from the secondaccess point, the hash identifier in a header associated with trafficfrom the second access point, wherein the second access point inheritsthe hash identifier based on being assigned to the RF neighborhood.

In some embodiments, each of the access point members of the dynamicallydetermined RF neighborhood self-monitors their surroundings.

In some embodiments, the telemetry includes one or more key performancemetrics associated with interference, noise, utilization, and radarperformance.

In some embodiments, the method further includes receiving the one ormore key performance metrics from the second access point and accesspoint members of the dynamically determined RF neighborhood, and basedon analyzing the one or more key performance metrics, determining a bestband, channel, channel width, or power for each access point within thedynamically determined RF neighborhood.

In some embodiments, the one or more key performance metrics iscontinuously updated as each of the access point members of thedynamically determined RF neighborhood send updates to the telemetry asradio frequency (RF) conditions change over time.

In some embodiments, a system for collecting access point telemetryincludes a first access point in communication with a single instance ona pod within a network, a second access point in communication with thenetwork, and a management service for determining radio frequency (RF)neighborhoods. In some embodiments, the management service identifiesthe first access point associated with a single instance on a pod,identifies a hash identifier for the RF neighborhood of the first accesspoint based on a geographical location of the first access point,dynamically determines subsequent access point members of the RFneighborhood by dynamically assigning the second access point to the RFneighborhood based on the second access point being within a thresholdgeographical location to the first access point, and directs telemetryfrom the second access point towards the single instance on the pod,where the pod receives telemetry for all access points in thedynamically determined RF neighborhood.

In some embodiments, a non-transitory computer-readable medium includesinstructions stored thereon, the instructions executable by one or moreprocessors of a computing system to identify a first access pointassociated with a single instance on a pod; identify a hash identifierfor a radio frequency (RF) neighborhood of the first access point basedon a geographical location of the first access point; dynamicallydetermine subsequent access point members of the RF neighborhood bydynamically assigning a second access point to the RF neighborhood basedon the second access point being within a threshold geographicallocation to the first access point; and direct telemetry from the secondaccess point towards the single instance on the pod, where the podreceives telemetry for all access points in the dynamically determinedRF neighborhood.

FIG. 1 illustrates an example network environment for collectingtelemetry from access points in accordance with some embodiments. System100 can allow network administrators to manage distributed multi-sitewireless networks with zero-touch provisioning, get network-widevisibility and control through a single portal, enforce automatic RFoptimizations via management service 110, and perform seamless rollingfirmware updates remotely and more. System 100 can provide powerful andintuitive cloud based centralized management with or without thecomplexity of traditional on-site Wireless LAN Controllers. In thisarchitecture, in addition to serving a client's many access points, theaccess points in system 100 can themselves continuously monitor theirsurroundings and report their findings to management service 110.

For example, system 100 can include management service 110 incommunication with one or more access points. In the example embodimentshown, management service 110 can be in communication with access point(AP) 112, AP 114, AP 116, and AP 118. System 100 can allow networkadministrators to logically group related APs into one or more “sites”,such as AP 112, AP 114, and AP 116 within site 120 and AP 118 withinsite 122.

In some embodiments, the access points within each site can bedynamically and automatically grouped into RF neighborhoods (RFNs). Forexample, in site 120, AP 112 has been dynamically grouped into RFN 126and AP 114 and AP 116 has been dynamically grouped into RFN 128.Likewise, AP 118 has been dynamically grouped into RFN 130 at site 122.Each RFN can determine how telemetry is routed from its member accesspoints. For example, telemetry can be routed across a portion of similarlinks from a specific RFN based on load considerations.

In some embodiments, management service 110 can be on the cloud, suchthat AP 112, AP 114, AP 116, and AP 118 communicate with managementservice 110 through internet 124. Management service can include one ormore pods that receive telemetry from access points belonging to aspecific RFN. For example, pod 132 can receive telemetry from RFN 126(e.g., for AP 112), pod 134 can receive telemetry from RFN 128 (e.g.,for AP 114 and AP 116), and pod N can receive telemetry from RFN 130(e.g., for AP 118). Each site can have one or more pods receivingtelemetry from its RFNs.

In some embodiments, management service 110 can receive and analyze RFreports from AP telemetry, including key metrics like interference,noise, utilization and radars, etc. Management service 110 can computethe best band, channel, channel width and power for every radio/AP in adeployment, and then implement any corresponding changes to how the APsare implemented—thus enabling APs to stay on top of changing RFconditions. As with any cloud based service, management service 110 canauto-scale with load, meaning there could be multiple instances of amanagement application running side-by-side, processing incoming data atany given time.

FIG. 2 is a flow chart illustrating an example embodiment of a telemetrymanagement service in accordance with some embodiments, which can beimplemented by components similar to that of the systems of FIGS. 3 and4. FIG. 3 illustrates example radio frequency (RF) neighborhoods inaccordance with some embodiments, and FIG. 4 illustrates an examplenetwork environment for collecting telemetry from access points inaccordance with some embodiments.

Network 402 can include management service 428, where management service428 can include pod 430, pod 432, pod 434, and pod 436 in communicationwith access points (APs) AP 404, AP 406, AP 408, AP 410, AP 412, AP 414,AP 416, AP 418, AP 420, AP 422, AP 424, and AP 426. The pods can be incommunication with the APs through load balancer service 450, which canbe neighborhood-aware such that it can receive telemetry data from theAPs and direct the telemetry data from all APs in a neighborhood (RFN)to the same service instance on a specific pod. Having all the relevanttelemetry data available at the same service instance can, for example,reduce the overall latency of the service.

In method 200, after a node within system 400 receives telemetry fromone or more APs, network 402 can load balance the telemetry from the oneor more APs across the pods of network 402 (e.g., by identifying a firstAP associated with a single instance on a pod) (step 210). For example,network 402 can include load balancer service 450 that can identify thattelemetry from a certain AP, such as AP 404, is to be directed towardspod 430, which can handle traffic to radio frequency neighborhood RFN-A438, which includes AP 404.

Management service 428 can determine and/or identify a hash identifierthat identifies the RFN of the AP based on a geographical location ofthe AP (step 212). For example, if AP 404 is the first AP within a siteor client network, then AP 404 can be assigned to RFN-A 438 based on itscurrent location within the client's buildings (e.g., its latitude andlongitudinal location as determined by GPS, manual addition, trilaterallocation techniques, etc.).

Subsequent access point members of the RFN can be dynamically determinedby automatically and dynamically assigning other APs to an RFN based onthe APs being within a threshold geographical location to the initial AP(step 214). For example, in FIG. 3, AP 302, 304, 306, 308, and 310 havebeen dynamically assigned to either RFN-A 312 or RFN-B 314. In thisembodiment, AP 302 was initially assigned to RFN-A 312. When AP 304 andAP 306 joined the network, they were assigned to RFN-A 312 because theywere within a certain distance 320 to AP 302 (e.g., 80 m). Likewise, AP308 and AP 310 were assigned to RFN-B 314 because they were also withinthe threshold geographical location to each other, but outside of thethreshold geographical distance 320 to AP 302 (or the other APs withinRFN-A 312). As a result, all APs that are close to each other aredynamically grouped within the same RFN. This distance 320 can be setand/or changed by an administrator or a controller within the managementservice, and can account for any obstacles affecting the effect ofsignal strength with respect to distance.

In some embodiments, the threshold geographical location can be based onan identification by an AP (e.g., AP 306) that the AP can hear a signalfrom the first access point at or within a threshold strength. Forexample, the RFNs can be distinguished based on a signal strength of −80dBm—APs that receive less can define a new RFN while APs that experiencethat or a greater signal strength are dynamically grouped within theinitial RFN. Accordingly, since AP 304 and AP 306 experience greatersignal strength than −80 dBm with AP 302, they define RFN-A 312.Conversely, RFN-B 314 includes AP 308 and 310, which experiences lesssignal strength than −80 dBm with AP 302. The threshold strength can beset and/or changed by an administrator or a controller within themanagement service, and can account for any obstacles affecting thesignal strength.

In some embodiments, while the network administrator can logically grouprelated APs into a site (e.g., site 120 and site 122 of FIG. 1), APswithin a site can be automatically/dynamically grouped into RFNs throughNeighbor Discovery messages. All APs can periodically send NeighborMessages at full power and the lowest possible data rate to probe theedges of propagation. APs that belong to the same site that can heareach other at signals stronger than −80 dBm can be organized into an RFneighborhood, like that depicted in FIG. 3.

In FIG. 4, AP 404, AP 406, and AP 408 dynamically grouped themselvesinto RFN-A 438 based on the distance and/or signal strength between theAPs being within the threshold value (e.g., within 80 m or −80 dBm).Similarly, AP 410, AP 412, and AP 414 have dynamically groupedthemselves into RFN-B 440; AP 416 has dynamically grouped itself intoRFN-C 442; AP 418, AP 420, and AP 422 have dynamically groupedthemselves into RFN-D 444; and AP 424 and AP 426 have dynamicallygrouped themselves into RFN-E 446.

In some embodiments, load balancer service 450 can direct AP telemetry456 traffic to appropriate pods hosting the management service 402 basedon a RFN_ID 460 (RF Neighborhood Identifier) received from the AP.RFN_ID 460 for example can be generated using the AP's media accesscontrol address (MAC_ADDR), Slot ID, Internet Protocol address (IP_ADDR(IPv4 or IPv6)), and/or Site_ID.

In some embodiments, load balancer service 450 can distribute APtelemetry 456 traffic based on their subscription to the site servicesand available number of pods. For sites having a similar number of APloads, when the number of pods approximately equates to the number ofSites, load balancer service 450 can map all APs of the same site to thesame pod. This way, it can minimize inter-Access Point RF Telemetrydependencies for RRM computations. While this method can add incrementalbenefits compared to the traditional methods, it can suffer inperformance when the number of Wi-Fi APs belonging to the same sitegrows exponentially.

Therefore in some embodiments, RF awareness can be added to loadbalancer service 450. Based on the load prediction, when thousands ofAPs are subscribed to a single site, for example, associating a singlepod for all these APs would result in suboptimal operations. In order tosolve this problem, load balancer service 450 can employ a method oftagging a hash identifier (HASH_ID) based on the geographicalneighborhood using techniques like cookies. When an AP initiatestelemetry 456 traffic, load balancer service 450 can look at the RFN_ID460 sent in the header. In some embodiments, HASH_ID can be anidentifier passed to the first AP in the neighborhood, and the RFN_ID460 can be based on or a function of the HASH_ID.

For example, load balancer service 450 can distribute telemetry 456 froman RFN to a corresponding pod that handles telemetry from the RFN. Insome embodiments the pods can be associated with one or more RFNs. Forexample, pod 430 can receive telemetry 456 from the APs within RFN-A438, pod 432 can receive telemetry 456 from the APs within RFN-B 440,pod 434 can receive telemetry 456 from the APs within RFN-D 444, and pod436 can receive telemetry 456 from the APs within RFN-C 442 and RFN-E446. In some embodiments, management service 428 can auto-scale withload, meaning there could be multiple instances of a managementapplication (RRM App 452) running side-by-side, processing incoming dataat any given time

Once an AP joins or is detected by system 400, the load balancer service450 can receive, from the AP, the HASH_ID 542 in a header associatedwith telemetry from the AP (step 216). In some embodiments, the AP caninherit the HASH_ID 542 based on being assigned to a specific RFN. Theload balancer service 450 can receive and then pass telemetry associatedwith RFN_ID 460, which can be based/inherited from HASH_ID 542) to themanagement service 428, which can direct the telemetry to the correctpod. FIG. 5, for example, illustrates an example network environment forcollecting telemetry from access points based on RFN_ID 560 inaccordance with some embodiments.

System 500 can include multiple RFNs within site 502 (e.g., RFN-A 504,RFN-B 506, RFN-C 508, RFN-D 510, RFN-E 512, and RFN-Z 514). Each RFN canhave one or more APs within a threshold distance and/or signal strength.Like FIGS. 1 and 4, the RFNs can be in communication with network 516through load balancer service, which can direct telemetry from an RFN toa certain pod based on the AP's RFN_ID 560. For example, pod 520 can beassociated with RFN-A 504, pod 522 can be associated with RFN-B 506, pod524 can be associated with RFN-C 508, pod 526 can be associated withRFN-D 510, pod 528 can be associated with RFN-E 512, and pod 530 can beassociated with RFN-Z 514, each association based on a unique RFN_ID560. The AP's RFN_ID 560 can be sent within traffic headers to loadbalancer service 518 in order to direct telemetry to the appropriatepod.

Accordingly, in some embodiments a pod (e.g., pod 520) can receivetelemetry data from an assigned AP (e.g., AP 532), where each of the APsof the dynamically determined RFN can self-monitor its surroundings. Forexample, AP 532 can self-monitor surrounding APs based on theNeighborhood Discovery Protocol (NDP), and can dynamically associate anew AP (e.g., AP 534) with RFN-A 504 based on determining that it iswithin the threshold distance and/or signal strength.

The telemetry data 456 can include one or more key performance metricsassociated with interference, noise, utilization, and/or radarperformance. Any change in an AP's channel, channel width or Tx powercan immediately have an impact on other APs within the same RFN. Hence,Management Service's 428 channel and power plans can be furtheroptimized for the entire RFN based on monitoring the key performancemetrics on a continuous basis.

For green-field deployment at the very initial access point subscription(e.g., AP 532 within RFN-A 504), load balancer service 518 can querymapping service 536 within management service 538 that maintains aholistic view of all the current neighbor relationships in a neighbordatastore 534. If AP 532 doesn't belong to an existing RFN, and is notwithin range of an existing RFN, then a new HASH_ID 542 can be computedin order to create a new RFN around AP 532. Mapping service 536 can theneither redirect 544 the AP's stream to an existing pod with availableresources or convey the new HASH_ID 542 to load balancer service 518 forredirection. Furthermore, neighbor datastore 534 can enlist an end toend view of all bi-directional neighbors and enable an efficient searchwithin neighbor datastore 534 when a newer node is getting looked up inthe database.

In some embodiments, once the generated HASH_ID 542 is communicated backto AP 532, it can start advertising it in the Neighbor DiscoveryProtocol (NDP) in order to broadcast the new HASH_ID 542 to itsimmediate neighbors, such as AP 534, AP 536, AP 538, and AP 540. Thisway, any newer APs that get installed would identify a neighbor'sHASH_ID 542 within the RFN during its boot up scan. All subsequent Wi-FiAPs that belong to the local RFN of AP 532 would inherit its neighbor'sHASH_ID 542. As NDP is encrypted with 128-bit AES, it securelyprovisions neighbor's HASH against DDoS attacks. An RFN_ID 560 can bebased on the inherited HASH_ID 542 that would identify an associated APwith the neighborhood.

In order to avoid oversubscription to a single pod even within the samesite, in some embodiments RF Boundary conditions can be enforced toensure smaller localized RF Sectors within a single site. In the exampleabove, a customer with one large site that is expanded across multiplebuildings can form multiple RFN_IDs per local area, building or even perfloor to isolate disjointed RFNs into multiple RFNs. Each RFN would haveone anchor node/AP that gets a unique HASH_ID 542 based on the anchorAP's identity. All other APs that belong to the RFN would result ininheriting the anchor node's/AP's HASH_ID 542.

Load balancer service 518 can then simply steer different APs with acustom HASH_ID 542 or RFN_ID 560, or can redirect any APs in apotentially newer RFN to mapping service 536 to have it assigned to anewer hash. Hence load balancer service 518 can distribute APs based onthe newer RF Neighborhood based HASH_ID 542 and therefore promoteefficient load-balancing for larger scale deployments with potentiallymillions of nodes or more.

In some embodiments load balancer service 518 can receive, from AP 534,the RFN_ID 560 in a header associated with telemetry from AP 534, whereAP 534 has inherited the HASH_ID 542 based on being assigned to RFN-A504 (step 216). Load balancer service 518 can direct telemetry from AP534 towards the single instance on the pod 520 (step 218), where the pod520 receives telemetry from all access points in the dynamicallydetermined RFN-A 504. Telemetry, such as key performance metrics from APmembers of the dynamically determined RFN-A 504, can be received by loadbalancer service 518, and based on analyzing the one or more keyperformance metrics, pod 520 can determine a best band, channel, channelwidth, and/or power for each AP within the dynamically determined RFN-A504. The key performance metrics can be continuously updated as each ofthe AP members of the dynamically determined RFN-A 504 sends updates tothe telemetry data as radio frequency (RF) conditions change over time.

FIG. 6 shows an example of computing system 600 in which the components,such as the components of FIGS. 1, 3, 5, and 5, of the system are incommunication with each other using connection 605. Connection 605 canbe a physical connection via a bus, or a direct connection intoprocessor 610, such as in a chipset architecture. Connection 605 canalso be a virtual connection, networked connection, or logicalconnection.

In some embodiments computing system 600 is a distributed system inwhich the functions described in this disclosure can be distributedwithin a datacenter, multiple datacenters, a peer network, etc. In someembodiments, one or more of the described system components representsmany such components each performing some or all of the function forwhich the component is described. In some embodiments, the componentscan be physical or virtual devices.

Example system 600 includes at least one processing unit (CPU orprocessor) 610 and connection 605 that couples various system componentsincluding system memory 615, such as read only memory (ROM) and randomaccess memory (RAM) to processor 610. Computing system 600 can include acache of high-speed memory connected directly with, in close proximityto, or integrated as part of processor 610.

Processor 610 can include any general purpose processor and a hardwareservice or software service, such as services 632, 634, and 636 storedin storage device 630, configured to control processor 610 as well as aspecial-purpose processor where software instructions are incorporatedinto the actual processor design. Processor 610 may essentially be acompletely self-contained computing system, containing multiple cores orprocessors, a bus, memory controller, cache, etc. A multi-core processormay be symmetric or asymmetric.

To enable user interaction, computing system 600 includes an inputdevice 645, which can represent any number of input mechanisms, such asa microphone for speech, a touch-sensitive screen for gesture orgraphical input, keyboard, mouse, motion input, speech, etc. Computingsystem 600 can also include output device 635, which can be one or moreof a number of output mechanisms known to those of skill in the art. Insome instances, multimodal systems can enable a user to provide multipletypes of input/output to communicate with computing system 600.Computing system 600 can include communications interface 640, which cangenerally govern and manage the user input and system output. There isno restriction on operating on any particular hardware arrangement andtherefore the basic features here may easily be substituted for improvedhardware or firmware arrangements as they are developed.

Storage device 630 can be a non-volatile memory device and can be a harddisk or other types of computer readable media which can store data thatare accessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs), read only memory (ROM), and/or somecombination of these devices.

The storage device 630 can include software services, servers, services,etc., that when the code that defines such software is executed by theprocessor 610, it causes the system to perform a function. In someembodiments, a hardware service that performs a particular function caninclude the software component stored in a computer-readable medium inconnection with the necessary hardware components, such as processor610, connection 605, output device 635, etc., to carry out the function.

For clarity of explanation, in some instances the present technology maybe presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

Any of the steps, operations, functions, or processes described hereinmay be performed or implemented by a combination of hardware andsoftware services or services, alone or in combination with otherdevices. In some embodiments, a service can be software that resides inmemory of a client device and/or one or more servers of a contentmanagement system and perform one or more functions when a processorexecutes the software associated with the service. In some embodiments,a service is a program, or a collection of programs that carry out aspecific function. In some embodiments, a service can be considered aserver. The memory can be a non-transitory computer-readable medium.

In some embodiments the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer readable media. Such instructions can comprise,for example, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, solid state memory devices, flash memory, USB devices providedwith non-volatile memory, networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include servers,laptops, smart phones, small form factor personal computers, personaldigital assistants, and so on. Functionality described herein also canbe embodied in peripherals or add-in cards. Such functionality can alsobe implemented on a circuit board among different chips or differentprocesses executing in a single device, by way of further example.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are means for providing the functions described inthese disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

1. A method for collecting access point telemetry comprising:identifying a first access point at a site associated with a singleinstance on a pod of a management service, wherein the site and themanagement service are connected via a network; identifying a hashidentifier for a radio frequency (RF) neighborhood of the first accesspoint at the site based on a geographical location of the first accesspoint at the site, wherein the RF neighborhood is one of a plurality ofRF neighborhoods at the site, each RF neighborhood of the plurality ofRF neighborhoods including at least one access point; dynamicallydetermining subsequent access point members of the RF neighborhood bydynamically assigning a second access point to the RF neighborhood ofthe plurality of RF neighborhoods at the site based on the second accesspoint being within a threshold geographical location to the first accesspoint at the site; and directing telemetry from the second access pointtowards the single instance on the pod, wherein the pod receivestelemetry for access points in the dynamically determined RFneighborhood.
 2. The method of claim 1, wherein the thresholdgeographical location is based on an identification by the second accesspoint that the second access point can hear a signal from the firstaccess point at a threshold strength.
 3. The method of claim 1, themethod further comprising: receiving, from the second access point, thehash identifier in a header associated with traffic from the secondaccess point, wherein the second access point inherits the hashidentifier based on being assigned to the RF neighborhood.
 4. The methodof claim 1, wherein each of the access point members of the dynamicallydetermined RF neighborhood self-monitors their surroundings.
 5. Themethod of claim 1, wherein the telemetry includes one or more keyperformance metrics associated with interference, noise, utilization,and radar performance.
 6. The method of claim 5, further comprising:receiving the one or more key performance metrics from the second accesspoint and access point members of the dynamically determined RFneighborhood; and based on analyzing the one or more key performancemetrics, determining a best band, channel, channel width, or power foreach access point within the dynamically determined RF neighborhood. 7.The method of claim 5, wherein the one or more key performance metricsis continuously updated as each of the access point members of thedynamically determined RF neighborhood send updates to the telemetry asradio frequency (RF) conditions change over time.
 8. A system forcollecting access point telemetry, the system comprising: a first accesspoint at a site in communication with a single instance on a pod of amanagement service within a network, wherein the site and the managementservice are connected via a network; a second access point incommunication with the network; and the management service fordetermining radio frequency (RF) neighborhoods, the management serviceto: identify the first access point associated with a single instance ona pod; identify a hash identifier for the RF neighborhood of the firstaccess point at the site based on a geographical location of the firstaccess point at the site, wherein the RF neighborhood is one of aplurality of RF neighborhoods at the site, each RF neighborhood of theplurality of RF neighborhoods including at least one access point;dynamically determine subsequent access point members of the RFneighborhood by dynamically assigning the second access point to the RFneighborhood of the plurality of RF neighborhoods at the site based onthe second access point being within a threshold geographical locationto the first access point; and direct telemetry from the second accesspoint towards the single instance on the pod, wherein the pod receivestelemetry for access points in the dynamically determined RFneighborhood.
 9. The system of claim 8, wherein the thresholdgeographical location is based on an identification by the second accesspoint that the second access point can hear a signal from the firstaccess point at a threshold strength.
 10. The system of claim 8, whereinthe management service further: receives, from the second access point,the hash identifier in a header associated with traffic from the secondaccess point, wherein the second access point inherits the hashidentifier based on being assigned to the RF neighborhood.
 11. Thesystem of claim 8, wherein each of the access point members of thedynamically determined RF neighborhood self-monitors their surroundings.12. The system of claim 8, wherein the telemetry includes one or morekey performance metrics associated with interference, noise,utilization, and radar performance.
 13. The system of claim 12, whereinthe management service further: receives the one or more key performancemetrics from the second access point and access point members of thedynamically determined RF neighborhood; and based on analyzing the oneor more key performance metrics, determines a best band, channel,channel width, or power for each access point within the dynamicallydetermined RF neighborhood.
 14. The system of claim 12, wherein the oneor more key performance metrics is continuously updated as each of theaccess point members of the dynamically determined RF neighborhood sendupdates to the telemetry as radio frequency (RF) conditions change overtime.
 15. A non-transitory computer-readable medium comprisinginstructions stored thereon, the instructions executable by one or moreprocessors of a computing system to: identify a first access point at asite associated with a single instance on a pod of a management service,wherein the site and the management service are connected via a network;identify a hash identifier for a radio frequency (RF) neighborhood ofthe first access point at a site based on a geographical location of thefirst access point at the site, wherein the RF neighborhood is one of aplurality of RF neighborhoods at the site, each RF neighborhood of theplurality of RF neighborhoods including at least one access point site;dynamically determine subsequent access point members of the RFneighborhood by dynamically assigning a second access point to the RFneighborhood of the plurality of RF neighborhoods at the site based onthe second access point being within a threshold geographical locationto the first access point at a site; and direct telemetry from thesecond access point towards the single instance on the pod, wherein thepod receives telemetry for access points in the dynamically determinedRF neighborhood.
 16. The non-transitory computer-readable medium ofclaim 15, wherein the threshold geographical location is based on anidentification by the second access point that the second access pointcan hear a signal from the first access point at a threshold strength.17. The non-transitory computer-readable medium of claim 15, theinstructions further executable by one or more processors of a computingsystem to: receive, from the second access point, the hash identifier ina header associated with traffic from the second access point, whereinthe second access point inherits the hash identifier based on beingassigned to the RF neighborhood.
 18. The non-transitorycomputer-readable medium of claim 15, wherein each of the access pointmembers of the dynamically determined RF neighborhood self-monitorstheir surroundings.
 19. The non-transitory computer-readable medium ofclaim 15, wherein the telemetry includes one or more key performancemetrics associated with interference, noise, utilization, and radarperformance.
 20. The non-transitory computer-readable medium of claim19, the instructions further executable by one or more processors of acomputing system to: receive the one or more key performance metricsfrom the second access point and access point members of the dynamicallydetermined RF neighborhood; and based on analyzing the one or more keyperformance metrics, determine a best band, channel, channel width, orpower for each access point within the dynamically determined RFneighborhood.